WO2010081836A1 - Procédé pour calculer un trajet économe en énergie - Google Patents

Procédé pour calculer un trajet économe en énergie Download PDF

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Publication number
WO2010081836A1
WO2010081836A1 PCT/EP2010/050363 EP2010050363W WO2010081836A1 WO 2010081836 A1 WO2010081836 A1 WO 2010081836A1 EP 2010050363 W EP2010050363 W EP 2010050363W WO 2010081836 A1 WO2010081836 A1 WO 2010081836A1
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WIPO (PCT)
Prior art keywords
energy cost
speed
longitudinal speed
lsps
energy
Prior art date
Application number
PCT/EP2010/050363
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English (en)
Inventor
Stephen T'siobbel
Edwin Bastiaensen
Robert Van Essens
Volker Hiestermann
Original Assignee
Tele Atlas B.V.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from GB0900659A external-priority patent/GB0900659D0/en
Priority claimed from GB0900678A external-priority patent/GB0900678D0/en
Application filed by Tele Atlas B.V. filed Critical Tele Atlas B.V.
Priority to EP10702637.9A priority Critical patent/EP2387699B1/fr
Priority to US13/144,959 priority patent/US8290695B2/en
Publication of WO2010081836A1 publication Critical patent/WO2010081836A1/fr
Priority to US13/616,014 priority patent/US8712676B2/en

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3469—Fuel consumption; Energy use; Emission aspects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks

Definitions

  • This invention relates to digital maps of the type for displaying road or pathway information, and more particularly toward a method for supplementing a digital map with data to enable various traffic modeling actions and to calculate an energy efficient route that can be offered to a driver.
  • Personal navigation devices like that shown generally at 10 in Figure 1 , for example, utilize digital maps combined with accurate positioning data from GPS or other data streams. These devices 10 have been developed for commuters seeking navigation assistance, for businesses trying to minimize transportation costs, and many other useful applications.
  • the effectiveness of such navigation systems is inherently dependent upon the accuracy and completeness of the information provided to it in the forms of digital maps and associated features and attribute data. Likewise, the effectiveness of such navigation systems is also dependent upon accurately and quickly matching the actual, real- world location of the navigation device to a corresponding portion of the digital map.
  • a navigation system 10 includes a display screen 12 or graphic user interface that portrays a network of streets as a series of line segments, including a center line running approximately along the center of each street or path, as exemplified in Figure 1. The traveler can then be generally located on the digital map close to or with regard to that center line.
  • GPS-enabled personal navigation devices such as those manufactured by TomTom N. V . (www.tonitom.com), may be also configured as probes to record its position at regular intervals.
  • probe data points comprise a sequence of discrete positions recorded at a particular time of the day taken at intervals of, for example, one second.
  • other suitable devices may be used to generate probe data points including handheld devices, mobile phones, PDAs, and the like.
  • Navigation devices are well known for their ability to plan a route between two locations in a digital map. For example, as shown in Figure 2, a traveler originating in Detroit may select a destination of Los Angeles in the digital map and activate an algorithm to calculate a route between the two locations. When alternate routes are possible, such route planning may be carried out on the basis of the shortest distance between origination and destination points. Or, if links in the network include associated travel time attributes, it is possible to recommend the route which indicates the shortest travel time. Other variables may include planning a route based on points of interest, and the like.
  • Some prior art devices have proposed the calculation of a route between origination and destination points based on fuel economy, carbon footprint and fuel pricing.
  • the ecoRouteTM offered by Garmin Ltd. uses information from a particular vehicle profile to calculate a fuel consumption estimation. That is, the user inputs details about their specific vehicle's fuel economy in both city and highway settings, selects a fuel type relative to the vehicle, and perhaps provides additional details. The system algorithm then calculates fuel consumption estimates based upon the distance to be traveled along a planned route.
  • One particular shortcoming of this approach is that it does not rely on any speed or acceleration attribute associated with the network of links in a digital map database. Therefore, the ecoRouting function is not particularly useful as a representative planning tool.
  • a driver wishing to travel between Detroit and Los Angeles is not able to intelligently assess the most economical route to travel.
  • programs like the ecoRouteTM require some burdensome user interaction with the navigation device and user knowledge about the vehicle characteristics, fuel prices, etc.
  • the input to be processed consists of recorded GPS traces, perhaps in the form of a standard ASCII stream or binary file.
  • the output may be a road map in the form of a directed graph with nodes and links associated with travel time information.
  • the probe data which creates the nodes or probe positions at regular intervals, can be transmitted to a collection service or other map making or data analysis service.
  • a collection service or other map making or data analysis service Through this method, wherein large populations of probe data are analyzed, road geometry can be inferred and other features and attributes derived by appropriate algorithms.
  • Figure 3 is a representative example of raw probe data collected over a period of days from a downtown, city-center area of Ottawa, Canada. From this raw probe data, even an untrained eye can begin to discern road geometries.
  • Each data point represented in the illustration of Figure 3 includes information as to the particular time of day that the data point was recorded. Thus, while Figure 3 depicts only longitudinally and laterally dispersed position data, the recorded data also provides a time stamp for each position.
  • each individual probe may create a trace which can be analyzed for travel speeds, accelerations, stops, and the like.
  • Speed profiles therefore represent a continuous or semi-continuous averaged speed distribution of vehicles derived from probe information, driving along the same section of the road and direction. Speed profiles reflect speed variations per segment per time interval, but are not longitudinally distributed in the sense that they do not describe velocity variations along the length of a link or road segment. This information can be used by a navigation system as a cost factor in connection with calculating optimal routes and providing travel/arrival time estimates.
  • Static elements may include features that affect traffic speed including for example sharp bends in the road, traffic controls, and other measures that affect traffic speed as a matter of geometry.
  • Dynamic elements include traffic volumes which fluctuate during workdays with local rush hour conditions, and are affected by weekend travel, holidays and the like.
  • the invention provides a method for creating Longitudinal Speed Profile (LSP) data useful for various traffic modeling applications.
  • Probe data is collected from a plurality of probes traversing a road segment in the form of vehicular traffic flow. Each probe develops a respective probe trace comprising a sequence of discrete probe positions recorded at a particular time of day. Daily time spans are established, e.g., every five minutes, and the probe data is bundled for each time span.
  • the probe data is then utilized to obtain Longitudinal Speed Profiles (LSPs) for vehicles traversing the road segment during each time span.
  • LSPs Longitudinal Speed Profiles
  • LSPs Longitudinal Speed Profiles
  • the invention also contemplates a method for computing an energy efficient route between an origin location and a destination location in situations where a digital map includes a network of road segments or links extending between the origin and destination locations.
  • Probe data is collected from probes traversing the links and then bundled and processed to obtain the Longitudinal Speed Profiles (LSPs) for each time span.
  • LSPs Longitudinal Speed Profiles
  • an energy cost is calculated for at least one direction of travel supported by the link during each time span, so that a route can be planned between the origin and destination by analyzing the energy cost for alternative link combinations in the network and preferring those links which minimize the average energy consumption value.
  • LSPs Longitudinal Speed Profiles
  • a detailed energy cost along the links can be calculated in the direction of travel, and perhaps even by lane in multi-lane roads, such as by taking the first derivative of speed over time or acquiring specific sensor data as may be available. From this information, energy cost can be introduced and used by the routing algorithms in much the same way that current routing algorithms utilize other cost factors like travel time or distance information.
  • an energy cost parameter can be used in at least a basic capacity to predict or estimate energy/fuel consumption characteristics without resorting to vehicle specific information such as mass, frontal area, aerodynamic drag and the like. Therefore, while these other parameters can be useful in providing a more accurate energy cost for each link in the network, it is at the most basic level sufficient to utilize only an energy cost derived from the Longitudinal Speed Profiles (LSPs) and then using this energy cost information to plan out a route between two points in a digital map.
  • LSPs Longitudinal Speed Profiles
  • T hi s invention allows the user to plan routes containing less acceleration/deceleration points on the route and in addition to that possible lower travel speeds, thus allowing the engines to work in more efficient (closer to steady RPM) mode, which will deliver the wanted energy economy and also decrease pollution.
  • Navigation systems operating route planning software can have an option to enter the time one can spend driving a particular optional route and see how "greener" the route will be for every time setting. Or, the person can enter the parameter of how much longer in % to the fastest time the more energy efficient route is allowed to be (as an example of one possible time setting).
  • a distinct advantage of this invention for planning an eco-friendly route does not necessarily require any vehicle-specific information to derive useful results, although more accurate computations can be made with the addition of vehicle-specific information.
  • the proposed method of route planning thus takes advantage of generic knowledge about the efficiency of all vehicle engines/motors (that is, to drive with the fewest number of accelerations/decelerations) to deliver more economical routes.
  • this method is also applicable to routing services occurring off-board or being retrieved over the web, such as on mapping and routing web sites used by internet users.
  • Figure 1 is an exemplary view of a portable navigation system according to one embodiment of the subject invention including a display screen for presenting map data information and including a computer readable medium having navigation software recorded thereon;
  • Figure 2 is a sample portion of a digital map depicting alternative routes between
  • Figure 3 is an example of raw probe data reflecting latitudinal and longitudinal positions (i.e., relative to road centreline) collected from a downtown, city-center area of
  • Figure 4 is a flow diagram describing the derivation of a Raw Road Design Speed
  • RRDSL RRDSL
  • LRRDSL Legal Raw Road Design Speed Limit
  • FIG. 5 is a chart showing the derived Longitudinal Speed Profiles (LSPs) for a particular road segment (AB), for a particular direction of travel, during different time spans, in this example in 30-minute increments;
  • Figure 6 is a diagram representing the posted speed limit for several consecutive road segments (AB-IJ), together with the RRDSL (16) for the same road segments;
  • Figure 7 is a diagram as in Figure 6 but showing also the LRRDSL (17) for the same road segments (AB-IJ);
  • Figure 8 is a flow diagram describing the derivation of an Optimum Longitudinal
  • Figure 9 is a diagram as in Figure 7 but showing also the OLSP for the same road segments (AB-IJ);
  • Figure 10 is a simplified longitudinal speed diagram for a road segment AB, showing both the RRDSL and OLSP, with energy savings represented by the OLSP being shown as an energy difference between the curves;
  • Figure 11 describes one method for determining an energy cost for a road segment for a particular time span
  • Figure 12 illustrates another method for determining an energy cost for a road segment for a particular time span.
  • this invention pertains to position reading devices, navigation systems, ADAS systems with GNSS (Global Navigation Satellite System), and the digital maps used by navigation systems.
  • This invention is therefore applicable to all kinds of navigation systems, position reading devices and GNSS enabled units including, but not limited to, handheld devices, PDAs, mobile telephones with navigation software, and in-car navigation systems operating as removable or built-in devices.
  • the invention can be implemented in any type of standard navigation system available on the market, on mapping and navigation web sites/servers as far as energy efficient route planning is concerned, as well as suitable systems which may be developed in the future.
  • the navigation-capable device typically includes a computer readable medium having navigation software recorded thereon.
  • a microprocessor associated with the device may be programmed to provisionally match the navigation device to a particular road segment in a digital map and then to make an assessment whether the provisional match is reliable. If not reliable, the system may rely on other techniques to determine the position of the navigation-capable device, such an auxiliary inertial guidance system for example.
  • Such inertial guidance systems may also include other features such as a DMI (Distance Measurement Instrument), which is a form of odometer for measuring the distance traveled by the vehicle through the number of rotations of one or more wheels.
  • DMI Distance Measurement Instrument
  • IMUs Inertial measurement units
  • the processor inside the navigation device may be further connected to a receiver of broadband information, a digital communication network and/or a cellular network.
  • a microprocessor of the type provided with the navigation device according to this invention may comprise a processor carrying out arithmetic operations.
  • a processor is usually connected to a plurality of memory components including a hard disk, read only memory, electrically erasable programmable read only memory, and random access memory. However, not all of these memory types may be required.
  • the processor is typically connected to a feature for inputting instructions, data or the like by a user in the form of a keyboard, touch screen and/or voice converter.
  • the processor may further be connected to a communication network via a wireless connection, for instance the public switch telephone network, a local area network, a wide area network, the Internet or the like by means of a suitable input/output device.
  • the processor may be arranged to communicate as a transmitter with other communication devices through the network.
  • the navigation-capable device may transmit its coordinates, data and time stamps to an appropriate collection service and/or to a traffic service center.
  • this constant vehicle speed may be approximately 45-60 mph, however that range may vary from one vehicle type to another, as well as being influenced by environmental conditions, road geographies, and the like. It is further known that various road characteristics such as sharp turns, speed bumps, lane expansions/consolidations, traffic controls and other features can influence the ability to safely travel at a constant speed along a particular segment. For this reason, the subject invention provides new, detailed map content to be used in connection with the navigation software applications to provide optimal energy-efficient driving speed recommendations .
  • a Raw Road Design Speed Limit may be derived from the collected probe data, according to the steps outlined in Figure 4.
  • the first step is to identify the time frame during which free flow traffic (no congestion) occurs. Once this free flow time span is known, the probe data for that time span is bundled, and then statistically analyzed to derive the speed at every point along the link, i.e., the road segment. Alternatively to selecting an optimal time span, the probe data can be analyzed to identify the higher probe speeds regardless of the time span. This process of deriving the speed at every point along the link is carried out for every road segment (or as many segments as practical.
  • the RRDSL may be associated with its respective segment as an attribute.
  • the RRDSL represents the longitudinally variable (vehicle) speed at any location along a road section in one direction where no obstructions to traffic are observed.
  • the RRDSL for each road segment is either taken from probe data at a time span where free flow traffic conditions are observed, or taken from probe data possessing the highest speeds regardless of the time span. For many road segments, free flow conditions will occur in the early morning hours when the fewest number of vehicles are traveling the roads.
  • a speed profile (like that obtained from the TomTom IQ RoutesTM product) taken at the time of the least traffic congestion may be somewhat similar to the RRDSL for a given road segment, but the IQ RoutesTM speed profile will be a single average speed for the entire road segment whereas the RRDSL will typically have speed changes along the length of the road segment.
  • the RRDSL is thus characteristic for specific locations along a road link and renders all effects which physically restrict the vehicles from going faster. As the information is derived from vehicle probes and reflects true driving, it may at times exceed the legal speed restriction.
  • the RRDSL When the RRDSL is represented along a road in a continuous or semi continuous way, one could call it an undisturbed speed which, when driven, is influenced primarily by the physical attributes of the road segment (e.g., its geometry) and the posted speed limits (if any).
  • the RRDSL can therefore be classified an attribute of a road segment; it does not vary over time of day. Only when road construction changes or road furniture is changed, or probe statistics change, is the RRDSL expected to change. As an attribute, it is possible to consider future applications of this concept in which, for example, a percentage of the stored RRDSL could be taken in case weather/surface conditions are known. As probe data content and resolution improvements are available, lane and/or vehicle category dependencies may be represented in the RRDSL.
  • the RRDSL may reflect regulatory situations such as higher speed limit on left lane or lower speed limit for commercial vehicles, etc. That is, the RRDSL can optionally be dependent on the specific vehicle type, or more generalized in vehicle categories (e.g. Powered Two Wheeler, Heavy Truck, Light Commercial Vehicle or Passenger car).
  • the RRDSL is particularly useful for Advanced Driver Assistance (ADAS) and other driving control purposes.
  • ADAS Advanced Driver Assistance
  • the RRDSL is derived from selected and filtered probe data which has been collected during periods of time when traffic flow is at or near its lowest for a particular road segment, i.e., at free flow conditions, or which has demonstrated the highest speeds.
  • the RRDSL 16 is a function of the longitudinal profile, based on position along a road section and of the travel-based direction profile (i.e., f(p, d)).
  • f(p, d) travel-based direction profile
  • FIG. 5 shows exemplary Longitudinal Speed Profiles (LSPs) derived from probe data (like that of Figure 3) for a hypothetical road segment (AB), for a particular direction of travel, during consecutive 30-minute time spans.
  • LSPs Longitudinal Speed Profiles
  • AB hypothetical road segment
  • FIG. 5 shows exemplary Longitudinal Speed Profiles (LSPs) derived from probe data (like that of Figure 3) for a hypothetical road segment (AB), for a particular direction of travel, during consecutive 30-minute time spans.
  • LSPs describe longitudinally (i.e., in the direction of the road centreline) varying average speed distributions of vehicles derived from probe information, driving along the same section of the road and direction.
  • These LSPs describe velocity variations along the length of a link or road segment for a specified time span. For the time span(s) which coincide with free flow traffic conditions, the LSP will be equivalent to the RRDSL 16.
  • the LSPs are associated with the respective road segment and either stored in a
  • FIG. 6 is a sample chart depicting consecutive road segments AB, BC, ... IJ.
  • Each road segment has a legal speed limit which is recorded in the digital map as an attribute. These speed limits are represented by the heavy, horizontal lines occurring at 30, 50 and 75 km/h.
  • Broken line 16 represents the RRDSL for the same road segments (AB, BC, ... IJ) which has been developed by bundling probe data recorded during an optimal time span (e.g., 0200-0230) and then averaging the results. Variations in the RRDSL 16 speeds can be attributed to features and geometries and attributes associated with each road segment, as suggested along the upper margin of the illustration. Features such as good physical visibility e.g.
  • the RRDSL 16 can vary even within the context of a single road segment, is associated in the digital map with the particular road segments and made available to navigation-capable devices which utilize the digital map in an interactive manner.
  • a target driving speed derived by considering the dynamic environmental situation (e.g., degraded road surface conditions or poor weather) and calculating a fraction of the RRDSL.
  • the RRDSL 16 can be attributed to its associated road segment in a digital map database in various ways.
  • an RRDSL 16 can be represented and stored as a parametric curve as a function of distance, or perhaps as a set of discrete optimal speeds between which to linearly interpolate, or normalized variations (percentages) above and below a legal speed limit/artificial threshold, to name a few possibilities.
  • Those of skill in the field of digital map database construction and implementation will readily appreciate these and possible other suitable techniques how to represent and store an RRDSL 16 in a map database.
  • various averages can be stored in a digital map, and provided for different types of vehicles.
  • a sub attribute representing the statistical signal of the RRDSL 16 can be stored in the map as well. Either as an average value, or as a longitudinal varying representation along the road element.
  • a driver operating with a navigation-capable device is able to continually compare their current speed (derived from successive GPS coordinates of the current time, or optionally derived from in-car sensor data) with the undisturbed speeds represented by the RRDSL 16 for the particular road segment.
  • a percentage of the RRDSL 16 may be used instead of the actual derived speeds which is proportional to the degraded driving conditions.
  • the navigation device then provides successive instructions or suggestions to the driver in audible, visual and/or haptic form, so that the driver might alter their driving speed to match or more closely mimic the target speeds along the road segment on which the vehicle is currently traveling.
  • the driver can expect to optimize their use of fuel in the most realistic manner possible, because the free flow conditions (upon with the RRDSL 16 was derived) represent the closest to steady-speed operation taking into account the practical considerations of road geometry and other real-world factors that influence driving speeds. This not only reduces operating costs of the vehicle, but also reduces vehicle emissions to the atmosphere and can improve driver comfort by reducing driver stress and fatigue. In more advanced systems, including the so-called ADAS applications which partly automate or take over driving tasks, the navigation device may even take an active role in conforming the current speed to the RRDSL 16 speeds.
  • sensory signals e.g., audible, visual and/or haptic
  • the navigation device will activated by the navigation device if the current, instantaneous speed of the carrying vehicle exceeds the RRDSL 16 target speed by some threshold value.
  • a threshold value ⁇ 5 km/h, or a percentage (e.g., 10%) may be established.
  • the RRDSL 16 will at times exceed the posted legal speed limits for a particular road segment. It is possible, indeed perhaps even preferable, therefore to reduce the target speeds of the RRDSL 16 to the legal speed limit whenever it exceeds the established speed limit at any point along the particular road segment.
  • the target speeds may be capped at each point where it rises above the local legal speed limit, resulting in a so-called Legal Raw Road Design Speed Limit (LRRDSL) 17.
  • LRRDSL Legal Raw Road Design Speed Limit
  • use of the term "legal” in this context does not preclude strategic limitation of the RRDSL speeds for reasons other than compliance with local speed regulations.
  • road segments in some jurisdictions may not impose any upper speed limit. This is sometimes the case along sections of the Autobahn in Germany for example. Applying principles of this invention to such unrestricted sections of roadway may result in a distribution of probe speeds with a very large spread, e.g., real speeds between 100 kph and 200 kph.
  • an artificial maximum threshold that is mindful of fuel economy statistics.
  • an artificial maximum threshold of 110 kph might be established, and used to limit the LRRDSL 17 where ever it exceeds the artificial threshold.
  • OLSP Optimal Longitudinal Speed Profile
  • the flow chart of Figure 8 describes two alternative approaches to deriving the OLSP 18.
  • the OLSP 18 is derived on the basis of kinetic energy simulations for various vehicle types or categories.
  • the OLSP 18 is simply attributed to the respective road segment in the digital map.
  • the OLSP 18 can be computed dynamically, i.e., on the fly, on the basis of data specific to the vehicle.
  • the target speed dictated by the OLSP attribute 18 is then used as the standard against which current vehicle speed is compared.
  • an optional step "Dynamic real time parameter or coefficient e.g.
  • OLSP 18 can alternatively be applied to the RRDSL 16 or the LRRDSL 17.
  • the dynamic parameter could be manifested as an absolute delta speed, or a relative speed differential (i.e., a percentage) or speed that is categorised/indexed (e.g., low/med/high) to the OLSP 18 (or the RRDSL 16 or LRRDSL 17).
  • This dynamic parameter may be provided to the navigation device 10 so that the system can calculate navigation and driving guidance instructions taking into account the real time dynamic situation, relative to the free flow target speed indicated by the OLSP 18 (or the RRDSL 16 or LRRDSL 17).
  • information can be provided to the navigation device 10 identifying the cause of the change of the parameter (e.g. congestion, partly road/lane closure, road works, road surface conditions, visibility, weather, events and incidents, etc.)
  • the comparison is proactive, in the sense that it is made on the road segment ahead of the current position so that an appropriate sensory signal (e.g., visual, sound, haptic, etc.) can be issued, considered by the driver and reacted upon in time with the movement of the vehicle.
  • Figure 9 shows the diagram of Figure 7 superimposed with an OLSP 18.
  • the OLSP 18, like the RRDSL 16, is also a function of the longitudinal profile, based on position along a road section and of the travel-based direction (i.e., f(p, d)).
  • It may also be a function of vehicle category (passenger car, bus/truck, powered two-wheeler), and is also preferably, but not necessarily, a function of a regulation dependency (like the LRRDSL 17).
  • vehicle category passenger car, bus/truck, powered two-wheeler
  • a regulation dependency like the LRRDSL 17.
  • minimizing the energy spend over the road segment also reflects a speed which will be close to the legal speed limit on the higher road classes.
  • vehicle manufacturers typically optimize the power trains of their vehicles to be most efficient between 85-95% of their top speed, which nearly always reflects the legal speed or speed restrictions in the region.
  • the OLSP 18 is a continuous or semi- continuous averaged speed distribution of vehicles driving along the same road and direction, considering the RRDSL 16 or the LRRDSL 17, and minimizing the number of accelerations/decelerations but keeping close to the RRDSL 16 (or LRRDSL 17) when no junctions are approached.
  • the term "longitudinal" appearing in the OLSP refers to the (semi) continuous description of this information along a road's axis.
  • the highest average speed profiles are represented by the RRDSL 16.
  • the OLSP 18 can be calculated by investigating the changes in energy involved in the system.
  • Computing the OLSP 18 respects the difference between the need for acceleration changes to be as small as possible, and keeping a fluent profile whilst keeping the vehicle in a speed zone for which the manufacturer optimized the functioning of its power train.
  • Those of skill in the field will appreciate various methods to derive the OLSP 18 from the LRRDSL 17 (or if preferred from the RRDSL 16).
  • the optimal acceleration and decoration strategy there exist some models in the state of the art that can be well used for this purpose. In one approach, boundaries are set on acceleration values.
  • ARFCOM model can be found at: http ://www.transporl- links.org/transport links/filearca/publications/1 " 73 PA3639.pdf. Details about the ARTEMIS model can be found at: http : //www . epa. go v/ttn/chicf7confercncc/ei 18/session6/andrc.pd f.
  • the energy difference optimized by the OLSP 18 in relation to the RRDSL 16 is represented in Figure 10 by the shaded area.
  • the energy saved by observing the OLSP 18 rather than the RRDSL 16 is proportional to the available energy conservation.
  • Individual vehicles driving according to the OLSP 18 will be using less fuel.
  • the surrounding traffic will be influenced with the behaviour of the vehicles driving according to the recommendations based on the OLSP 18 (or an OLSP 18 enhanced with a dynamic parameter).
  • the OLSP 18 will not only impact the vehicles actually using the information but will also have a significant and beneficial secondary impact on surrounding traffic.
  • Personal navigation devices 10 like those described above are particularly efficient at comparing many different routes between an origination and destination location and determining the best possible or optimum route, as shown in Figure 2.
  • route planning algorithms consider a so-called cost attribute associated with each possible link in the network which is sought to be minimized or maximized by the navigation/route planning software.
  • This type of route planning technique is well known for determining the fastest, shortest or other cost criteria route between two points.
  • Using the technique of calculating the LSPs it is possible to derive also an energy cost for each link in the network for each time span.
  • Figure 11 shows one method by which a longitudinally distributed energy cost can be determined for any road segment, in this example road segment AB.
  • Energy Cost is calculated on the basis of the area under the LSP for the road segment AB for a time span. However, it may be useful in some cases to simply calculate a singe Energy Cost for the road segment AB which is not time dependent. In these special situations, the LRRDSL 17 may be used, as it represents the LSP for segment AB at the free flow time span. Or perhaps, a (pragmatically) optimal, time independent Energy Cost could be calculated on the basis of the OLSP 18, which also corresponds to the LSP for segment AB at the free flow time span.
  • the energy cost is preferably indexed to the time intervals for the LSPs, e.g., every five minutes or every half hour as in Fig. 5. Therefore, this energy cost would be derived from real traffic information as collected from the probe data, and thus incorporate dynamic aspects as well as static aspects attributable to road geometries and the like. However, it may be useful in some routing applications to consider only the special case of energy cost during free flow conditions, which can be derived from any of the RRDSL 16, LRRDSL 17 or OLSP 18 due to their correspondence with the LSP for the free flow time span.
  • the energy cost can be represented as cost information and associated directly with each link, i.e., with each segment between two nodes in a digital map, and thereby represent a cost criteria related to energy consumption over that link.
  • the energy cost is calculated at least from the speed and acceleration profiles (i.e., LSPs) obtained from probe data and is relative to other links on the map.
  • the average velocity profile and average acceleration profile are particularly relevant in view of the parametric approach to modeling vehicle energy consumption founded upon the well-known road load equation: p - p +p -p +p
  • Proad is the road load power (W), v is the vehicle speed (m/s), a is the vehicle acceleration (m/s2), p is the density of air ( ⁇ 1.2kg/m3),
  • CD is the aerodynamic drag coefficient
  • A is the frontal area (mz)
  • CRR is the rolling resistance coefficient
  • mtotai is the total vehicle mass (kg)
  • g is the gravitational acceleration (9.81m/s2)
  • Z is the road gradient (%) and km is a factor to account for the rotational inertia of the power train (Plotkin et al.
  • acceleration loads are typically more heavily weighted than resistance due to aerodynamic drag (P aer o) or resistance due to rolling (P ro u) or resistance due to gravitation forces (Pgrade).
  • the load due to acceleration includes the product of acceleration times velocity (av).
  • the speed-acceleration index will serve as a useful estimation tool so that routing algorithms can apply at least a simplified version of the average energy value as a cost and choose the best route between two locations in a digital map by attempting to minimize the energy loss.
  • An alternative technique for determining energy cost for a road segment is to take the first derivative of the LSP, which may be characterized as an acceleration profile. Using this acceleration profile, it is possible to keep track of the number of accelerations and decelerations above a set threshold. This count can then be assigned to a road segment.
  • Such an acceleration profile would provide a simplified was to store information that in turn can be used to compute the energy cost.
  • a routing algorithm would favor segments with high speed. On a higher level, the routing algorithm needs to identify chains of road segments with the overall minimum energy loss. This information can be used in navigation systems to select the least energy consuming route.
  • the LSPs (or the LRRDSL 17, OLSP 18, or even the RRDSL 16, for time independent applications) can be used as a predictive or routing function to find an economical route by considering it in routing algorithms.
  • the OLSP 18 can be used in conjunction with a suitable navigation device 10 to provide an instantaneous performance indicator by offering a reference signal to which real time comparisons can be made so as to advise the driver.
  • a suitable navigation device 10 to provide an instantaneous performance indicator by offering a reference signal to which real time comparisons can be made so as to advise the driver.
  • yet another method to compute the Energy Cost is presented as a velocity times acceleration index is plotted along the length of the link for a particular driving direction. As in the preceding alternate technique to determine the energy cost by creating an acceleration index, this method may also keep track of the number of peaks extending past a threshold.
  • the threshold is shown as horizontal broken lines spaced equidistant from the x-axis, with the respective peaks extending past the threshold shaded.
  • the acceleration is null, or zero (i.e., constant velocity)
  • the speed-acceleration index will be null/zero on the graph.
  • Positive or top speed peaks will be created as the speed-acceleration index resides in positive territory, whereas negative or minimum speed peaks are shown during deceleration modes with negative values presenting.
  • the energy cost can be complemented with the number of top and/or minimum peaks that exist along that link. This may be simply represented with an absolute number or expressed by some other efficient method. The number could be calculated using statistics on speed-acceleration indices derived from probe data on the link, for example.
  • an energy efficient routing application may include routing algorithms which compare the number of peaks and the magnitude of peaks along the speed-acceleration index between different links. Such a value could be automatically calculated and added to a digital map for each link complementary to the speed profiles and acceleration profiles provided in the time span divisions.
  • the energy cost associated with a particular road segment or link can be computed using many different techniques, including but not limited to those described here. Once computed, the energy cost may be compared on a link-by-link basis in the digital map to evaluate how much disturbance there is on any particular route. (Refer again to Figure 2.) Once a preferred route has been established, the navigation system 10 can provide navigation assistance to the driver along that route to obtain the further improved fuel economy by observing the OLSP 18 described above. Thus, not only will the driver be able to calculate the most energy efficient route between two points, the driver will also be assisted to drive in an even more efficient manner along the route which takes into account geometric and other static road features along the entire route.
  • computation of an energy efficient route is using an index of energy costs which is based on LSPs as this takes into account the time-dependent nature of speed distribution along road segments.
  • energy efficient routing uses energy costs based on OLSP 18, or alternatively RRDSL 16 or LRRDSL 17, either of which is not a time-dependent LSP but represents ideal (free-flow) traffic conditions. This allows achieving at least a basic level of energy efficient routing, in that alternate routes can still be compared on basis of overall energy cost. In this way, the lowest possible overall energy cost for a desired route can be established.
  • energy cost based on LPS and energy cost based RRDSL 16, LRRDSL 17, or OLSP 18 can be considered in a combined view, in that the energy cost determined through LSPs is compared to the lowest possible overall energy cost (i.e. energy cost based on RRDSL 16, LRRDSL 17, or OLSP 18).
  • Information about the efficiency comparison expressed as ratio, percentage, normalized score or other suitable measure, may be recorded or presented to the user (such as an actual efficiency score).
  • a user may preset an efficiency comparison target as part of the route planning exercise.
  • detailed speed and acceleration information can be calculated along each link in a network as derived from probe data in the form of time independent attributes of RRDSL 16, LRRDSL 17, and OLSP 18 or in the form of the time-dependant LSPs.
  • the energy consumption along the road can be approximated using various alternative techniques on the basis of one or more of these derived values.
  • additional parameters are known such as: vehicle mass, air density, aerodynamic drag, frontal area, rolling resistance, gravitational acceleration, road gradient and rotational inertia of the power train.
  • Calculating an energy cost may also include specialization by vehicle category, such as separate categories for trucks, passenger cars, buses, etc.
  • vehicle category-specific energy cost may then be derived from probe data bundled by vehicle category. In other words, probe data acquired from bus transits will be used to calculate an energy cost that is specific to buses, and so forth.
  • acceleration is the relative component to capture and assess the vehicle energy consumption. Changes in acceleration are quantified into a speed- acceleration index which is the product of vehicle acceleration and vehicle speed along the road link.
  • One way to quantify the acceleration impact over a road link is to calculate the area enclosed by the speed-times-acceleration function, both for the area with positive and with negative acceleration. This is described with reference to Figure 12 in which positive accelerations are shown above the null/zero line and negative accelerations below the null/zero line.
  • Another, additional value that may be stored as an attribute of the road link can be the sum of the number of acceleration energy peaks above (and below) a predefined threshold, as represented by the broken horizontal lines in Figure 12. (This can also be normalized over the length of the road link.) This would give a count of positive and negative acceleration energy peaks which, as stated previously, may be used to more fully develop an energy cost and provide an efficient estimating tool.
  • Aerodynamic resistance is also a valuable parameter.
  • the cube of vehicle velocity is of importance.
  • One approach may be to quantify the energy consumption due to aerodynamic drag using thresholds (e.g., 30km/h, 50km/h, 90km/h, 120km/h) and to measure the length in meters for each section delimited by the thresholds. For example, a road of lkm length, 250m is in the 30-50km/h, 500m above the 120km/h, and 250m in the 50-90km/h.
  • Rolling resistance is another parameter.
  • the vehicle energy consumption is governed by the vehicle speed as described by the energy load equation stated previously.
  • the quantification of this energy may be accomplished by adopting a similar approach as above - namely summing the length of the stretches of road where the vehicle speed falls within a specific category.
  • Other parameters to assess the rolling resistance parameter can be to estimate the rolling resistance coefficient (Cn), assume vehicle mass per class, or the like.
  • Loads due to road gradient are another factor. Vehicle mass may be assumed or given, and gravity is known. Therefore, the decisive parameters to quantify the energy consumption due to road gradient are the product of the road gradient and the velocity. The road gradient is or will be available in most digital map databases.
  • a formula to calculate or estimate the energy consumption more accurately over each link in the map database may include any or all of the components mentioned above, but in all cases includes at least the speed-acceleration index (i.e., LSPs) as defined.
  • LSPs speed-acceleration index
  • an energy efficient routing algorithm effectively makes an estimation using the speed profiles and acceleration profiles derived from probe data so that very accurate and useful route planning and navigation assistance can be provided.
  • an acceleration index can also be attributed to its associated road segment in the digital map database in various ways.
  • an acceleration index can be represented and stored in a map database by approximation of the positive and negative peaks in terms of their position along a link together with the respective vertical size and horizontal width, or as a parametric curve as a function of distance, or perhaps as a set of discrete optimal speeds between which to linearly interpolate, normalized over the road link length, etc.
  • a map database by approximation of the positive and negative peaks in terms of their position along a link together with the respective vertical size and horizontal width, or as a parametric curve as a function of distance, or perhaps as a set of discrete optimal speeds between which to linearly interpolate, normalized over the road link length, etc.
  • a vehicle speed reflecting an optimal, high efficiency speed is based on low traffic situations. Therefore, it is desirable to derive the attributes 16, 17, 18 from processing of other profiles resulting from a minimum amount of traffic.
  • the attributes 16, 17, 18 can be derived for different times spans on the basis of historic traffic situations using the derived LSP data.
  • the derived attributes will preferably include accelerations and decelerations witnessed by all vehicles and/or by specific vehicle types such as heavy trucks, delivery vans and the like. These attributes are preferably derived for a particular driving direction, i.e., for each lane of a multi-lane road segment, at a particular time span or interval.
  • this data reflects driving behavior, it implicitly includes speed adaptations caused by infrastructure (traffic lights, curvy road segments, speed bumps, etc.) and perhaps eventually also by expert drivers. That is, drivers whose cars are equipped with devices to enhance fuel economy as well as drivers who have studied eco-friendly driving styles. The emphasis of the contribution of the latter may be determined when the probe signal from which the speed profiles are derived will identify classes of drivers and/or vehicle characteristics.

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  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
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Abstract

Selon l'invention, des données de sonde sont analysées pour déduire des profils de vitesse longitudinale (LSP) et un profil de vitesse longitudinale optimale (18) pour chaque segment de route ou de liaison dans un réseau de carte numérique. Les profils des profils de vitesse longitudinale (LSP) sont calculés pendant des intervalles de temps définis alors que le profil de vitesse longitudinale optimale (18) se base sur le LSP pendant l'intervalle de temps correspondant seulement à des conditions de circulation en écoulement libre. Tous les LSP peuvent être utilisés pour créer un coût énergétique respectif pour chaque intervalle de temps, ou seulement l'OLSP (18) peut être utilisé (ou en variante le RRDSL 16 ou le LRRDSL 17) pour calculer un coût énergétique pour les conditions en écoulement libre seulement. Le coût énergétique peut être utilisé afin de prévoir l'énergie nécessaire à un véhicule pour parcourir la liaison. Un logiciel de navigation peut utiliser le coût énergétique pour planifier le trajet le plus efficace en énergie entre deux emplacements sur la carte numérique. Des signaux sensoriels peuvent être activés si un conducteur s'écarte du profil de vitesse longitudinale optimale (18) pour aboutir à des niveaux extrêmement élevés d'efficacité énergétique.
PCT/EP2010/050363 2009-01-16 2010-01-13 Procédé pour calculer un trajet économe en énergie WO2010081836A1 (fr)

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US10161758B2 (en) 2018-12-25
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US8712676B2 (en) 2014-04-29
US20130245943A1 (en) 2013-09-19

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